#loading the required libraries
import numpy as np
import pandas as pd
import matplotlib as plt
import seaborn as sns
import plotly.express as px

#loading the data sets by pandas
df_ipl = pd.read_csv('matches.csv')

#checking the top 5 records 
df_ipl.head()

	id	season	city	date	team1	team2	toss_winner	toss_decision	result	dl_applied	winner	win_by_runs	win_by_wickets	player_of_match	venue	umpire1	umpire2	umpire3
0	1	2017	Hyderabad	05-04-2017	Sunrisers Hyderabad	Royal Challengers Bangalore	Royal Challengers Bangalore	field	normal	0	Sunrisers Hyderabad	35	0	Yuvraj Singh	Rajiv Gandhi International Stadium, Uppal	AY Dandekar	NJ Llong	NaN
1	2	2017	Pune	06-04-2017	Mumbai Indians	Rising Pune Supergiant	Rising Pune Supergiant	field	normal	0	Rising Pune Supergiant	0	7	SPD Smith	Maharashtra Cricket Association Stadium	A Nand Kishore	S Ravi	NaN
2	3	2017	Rajkot	07-04-2017	Gujarat Lions	Kolkata Knight Riders	Kolkata Knight Riders	field	normal	0	Kolkata Knight Riders	0	10	CA Lynn	Saurashtra Cricket Association Stadium	Nitin Menon	CK Nandan	NaN
3	4	2017	Indore	08-04-2017	Rising Pune Supergiant	Kings XI Punjab	Kings XI Punjab	field	normal	0	Kings XI Punjab	0	6	GJ Maxwell	Holkar Cricket Stadium	AK Chaudhary	C Shamshuddin	NaN
4	5	2017	Bangalore	08-04-2017	Royal Challengers Bangalore	Delhi Daredevils	Royal Challengers Bangalore	bat	normal	0	Royal Challengers Bangalore	15	0	KM Jadhav	M Chinnaswamy Stadium	NaN	NaN	NaN

#looking the number of rows and columns in the data set
df_ipl.shape

(756, 18)

#Getting the frequency of most man of the match awards and ploting it 
manofthematch=df_ipl['player_of_match'].value_counts().head(10)
fig=px.bar(manofthematch, y='player_of_match', color='player_of_match')
fig.show(renderer='notebook')

df_ipl.head(2)

	id	season	city	date	team1	team2	toss_winner	toss_decision	result	dl_applied	winner	win_by_runs	win_by_wickets	player_of_match	venue	umpire1	umpire2	umpire3
0	1	2017	Hyderabad	05-04-2017	Sunrisers Hyderabad	Royal Challengers Bangalore	Royal Challengers Bangalore	field	normal	0	Sunrisers Hyderabad	35	0	Yuvraj Singh	Rajiv Gandhi International Stadium, Uppal	AY Dandekar	NJ Llong	NaN
1	2	2017	Pune	06-04-2017	Mumbai Indians	Rising Pune Supergiant	Rising Pune Supergiant	field	normal	0	Rising Pune Supergiant	0	7	SPD Smith	Maharashtra Cricket Association Stadium	A Nand Kishore	S Ravi	NaN

#checking the various types of the results
resultplot=df_ipl['result'].value_counts()
px.bar(y=resultplot.index,x=resultplot.values,color=resultplot.index, color_discrete_sequence=px.colors.qualitative.Plotly)

df_ipl.head(2)

	id	season	city	date	team1	team2	toss_winner	toss_decision	result	dl_applied	winner	win_by_runs	win_by_wickets	player_of_match	venue	umpire1	umpire2	umpire3
0	1	2017	Hyderabad	05-04-2017	Sunrisers Hyderabad	Royal Challengers Bangalore	Royal Challengers Bangalore	field	normal	0	Sunrisers Hyderabad	35	0	Yuvraj Singh	Rajiv Gandhi International Stadium, Uppal	AY Dandekar	NJ Llong	NaN
1	2	2017	Pune	06-04-2017	Mumbai Indians	Rising Pune Supergiant	Rising Pune Supergiant	field	normal	0	Rising Pune Supergiant	0	7	SPD Smith	Maharashtra Cricket Association Stadium	A Nand Kishore	S Ravi	NaN

#checking the null values 
df_ipl.isnull().sum().sort_values(ascending=False)

umpire3            637
city                 7
winner               4
player_of_match      4
umpire2              2
umpire1              2
venue                0
win_by_wickets       0
win_by_runs          0
id                   0
season               0
result               0
toss_decision        0
toss_winner          0
team2                0
team1                0
date                 0
dl_applied           0
dtype: int64

#checking the persentage to take a deision 
(df_ipl.isnull().mean()*100).round(2).sort_values(ascending = False)

umpire3            84.26
city                0.93
winner              0.53
player_of_match     0.53
umpire2             0.26
umpire1             0.26
venue               0.00
win_by_wickets      0.00
win_by_runs         0.00
id                  0.00
season              0.00
result              0.00
toss_decision       0.00
toss_winner         0.00
team2               0.00
team1               0.00
date                0.00
dl_applied          0.00
dtype: float64

#droping unwanted columns
df_ipl = df_ipl.drop(columns=['umpire3'])

df_ipl.head(2)

	id	season	city	date	team1	team2	toss_winner	toss_decision	result	dl_applied	winner	win_by_runs	win_by_wickets	player_of_match	venue	umpire1	umpire2
0	1	2017	Hyderabad	05-04-2017	Sunrisers Hyderabad	Royal Challengers Bangalore	Royal Challengers Bangalore	field	normal	0	Sunrisers Hyderabad	35	0	Yuvraj Singh	Rajiv Gandhi International Stadium, Uppal	AY Dandekar	NJ Llong
1	2	2017	Pune	06-04-2017	Mumbai Indians	Rising Pune Supergiant	Rising Pune Supergiant	field	normal	0	Rising Pune Supergiant	0	7	SPD Smith	Maharashtra Cricket Association Stadium	A Nand Kishore	S Ravi

#treating missing values 
df_ipl['city'].replace(np.NaN,'Unknown',inplace=True)

df_ipl['winner'].replace(np.NaN,'Unknown',inplace=True)

df_ipl['player_of_match'].replace(np.NaN,'Unknown',inplace=True)

mode_value = df_ipl["umpire2"].mode()[0]

df_ipl['umpire2'].fillna(mode_value,inplace=True)

mode_value1 = df_ipl["umpire1"].mode()[0]

df_ipl['umpire1'].fillna(mode_value1,inplace=True)

df_ipl.isnull().sum().sort_values(ascending=False)

player_of_match    4
id                 0
dl_applied         0
umpire1            0
venue              0
win_by_wickets     0
win_by_runs        0
winner             0
result             0
season             0
toss_decision      0
toss_winner        0
team2              0
team1              0
date               0
city               0
umpire2            0
dtype: int64

df_ipl[df_ipl['player_of_match'].isnull()]

	id	season	city	date	team1	team2	toss_winner	toss_decision	result	dl_applied	winner	win_by_runs	win_by_wickets	player_of_match	venue	umpire1	umpire2
300	301	2011	Delhi	21-05-2011	Delhi Daredevils	Pune Warriors	Delhi Daredevils	bat	no result	0	Unknown	0	0	NaN	Feroz Shah Kotla	SS Hazare	RJ Tucker
545	546	2015	Bangalore	29-04-2015	Royal Challengers Bangalore	Rajasthan Royals	Rajasthan Royals	field	no result	0	Unknown	0	0	NaN	M Chinnaswamy Stadium	JD Cloete	PG Pathak
570	571	2015	Bangalore	17-05-2015	Delhi Daredevils	Royal Challengers Bangalore	Royal Challengers Bangalore	field	no result	0	Unknown	0	0	NaN	M Chinnaswamy Stadium	HDPK Dharmasena	K Srinivasan
744	11340	2019	Bengaluru	30-04-2019	Royal Challengers Bangalore	Rajasthan Royals	Rajasthan Royals	field	no result	0	Unknown	0	0	NaN	M. Chinnaswamy Stadium	Nigel Llong	Ulhas Gandhe

#checking the data and datatypes of the columns
df_ipl.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 756 entries, 0 to 755
Data columns (total 17 columns):
 #   Column           Non-Null Count  Dtype   
---  ------           --------------  -----   
 0   id               756 non-null    int64   
 1   season           756 non-null    int64   
 2   city             756 non-null    object  
 3   date             756 non-null    object  
 4   team1            756 non-null    object  
 5   team2            756 non-null    object  
 6   toss_winner      756 non-null    object  
 7   toss_decision    756 non-null    object  
 8   result           756 non-null    object  
 9   dl_applied       756 non-null    int64   
 10  winner           756 non-null    object  
 11  win_by_runs      756 non-null    int64   
 12  win_by_wickets   756 non-null    int64   
 13  player_of_match  752 non-null    category
 14  venue            756 non-null    object  
 15  umpire1          756 non-null    object  
 16  umpire2          756 non-null    object  
dtypes: category(1), int64(5), object(11)
memory usage: 106.0+ KB

#changing the data type of the columns
df_ipl['player_of_match']=df_ipl['player_of_match'].astype('object')

df_ipl.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 756 entries, 0 to 755
Data columns (total 17 columns):
 #   Column           Non-Null Count  Dtype 
---  ------           --------------  ----- 
 0   id               756 non-null    int64 
 1   season           756 non-null    int64 
 2   city             756 non-null    object
 3   date             756 non-null    object
 4   team1            756 non-null    object
 5   team2            756 non-null    object
 6   toss_winner      756 non-null    object
 7   toss_decision    756 non-null    object
 8   result           756 non-null    object
 9   dl_applied       756 non-null    int64 
 10  winner           756 non-null    object
 11  win_by_runs      756 non-null    int64 
 12  win_by_wickets   756 non-null    int64 
 13  player_of_match  752 non-null    object
 14  venue            756 non-null    object
 15  umpire1          756 non-null    object
 16  umpire2          756 non-null    object
dtypes: int64(5), object(12)
memory usage: 100.5+ KB

#making sure the we have not missed to treat any null values 
df_ipl.isnull().sum().sort_values(ascending=False)

id                 0
dl_applied         0
umpire1            0
venue              0
player_of_match    0
win_by_wickets     0
win_by_runs        0
winner             0
result             0
season             0
toss_decision      0
toss_winner        0
team2              0
team1              0
date               0
city               0
umpire2            0
dtype: int64

#Checking which team won the toss most times
df_ipl["toss_winner"].value_counts().head()

Mumbai Indians                 98
Kolkata Knight Riders          92
Chennai Super Kings            89
Royal Challengers Bangalore    81
Kings XI Punjab                81
Name: toss_winner, dtype: int64

#checking which team bat first and won the match with the plot below 
batingfirst = df_ipl[df_ipl['win_by_runs']!=0]

fig = px.histogram(batingfirst['win_by_runs'])
fig.update_layout(title_text='Distribution by Runs')
fig.update_xaxes(title_text='Runs')
fig.update_yaxes(title_text='Match_count')

#Finding out the number of wins each team after batting first
batingfirst["winner"].value_counts()

Mumbai Indians                 57
Chennai Super Kings            52
Kings XI Punjab                38
Kolkata Knight Riders          36
Royal Challengers Bangalore    35
Sunrisers Hyderabad            30
Rajasthan Royals               27
Delhi Daredevils               25
Deccan Chargers                18
Pune Warriors                   6
Rising Pune Supergiant          5
Delhi Capitals                  3
Kochi Tuskers Kerala            2
Rising Pune Supergiants         2
Gujarat Lions                   1
Name: winner, dtype: int64

#checking the second bating and who wont the match most by wickets with plots 
secondbating = df_ipl[df_ipl['win_by_wickets']!=0]

fig = px.histogram(secondbating['win_by_wickets'])
fig.update_layout(title_text='Distribution by Runs')
fig.update_xaxes(title_text='Wikets')
fig.update_yaxes(title_text='Match_count')

#secondbated and wont the most match
secondbating['winner'].value_counts()

Kolkata Knight Riders          56
Mumbai Indians                 50
Royal Challengers Bangalore    48
Chennai Super Kings            48
Rajasthan Royals               46
Kings XI Punjab                42
Delhi Daredevils               42
Sunrisers Hyderabad            27
Gujarat Lions                  12
Deccan Chargers                11
Pune Warriors                   6
Delhi Capitals                  6
Rising Pune Supergiant          5
Kochi Tuskers Kerala            4
Rising Pune Supergiants         3
Name: winner, dtype: int64

#looking at the number of the matches played each season
df_ipl['season'].value_counts()

2013    76
2012    74
2011    73
2010    60
2014    60
2016    60
2018    60
2019    60
2017    59
2015    59
2008    58
2009    57
Name: season, dtype: int64

#looking at the number of the matches played each city
df_ipl['city'].value_counts()

Mumbai            101
Kolkata            77
Delhi              74
Bangalore          66
Hyderabad          64
Chennai            57
Jaipur             47
Chandigarh         46
Pune               38
Durban             15
Bengaluru          14
Visakhapatnam      13
Ahmedabad          12
Centurion          12
Rajkot             10
Mohali             10
Indore              9
Dharamsala          9
Johannesburg        8
Cape Town           7
Ranchi              7
Abu Dhabi           7
Cuttack             7
Unknown             7
Port Elizabeth      7
Sharjah             6
Raipur              6
Kochi               5
Kanpur              4
Nagpur              3
Kimberley           3
East London         3
Bloemfontein        2
Name: city, dtype: int64

#Checking the number of team those who won the toss also and match also 
np.sum(df_ipl['toss_winner']==df_ipl['winner'])

393

#As it is proved toss dose not played a main role to win the match
result = 393/756
percentage = result * 100
print(percentage)

51.98412698412699

#importing other data set
deliveries = pd.read_csv("deliveries.csv")

#seeing top 5 records
deliveries.head()

	match_id	inning	batting_team	bowling_team	over	ball	batsman	non_striker	bowler	is_super_over	...	bye_runs	legbye_runs	noball_runs	penalty_runs	batsman_runs	extra_runs	total_runs	player_dismissed	dismissal_kind	fielder
0	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	1	DA Warner	S Dhawan	TS Mills	0	...	0	0	0	0	0	0	0	NaN	NaN	NaN
1	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	2	DA Warner	S Dhawan	TS Mills	0	...	0	0	0	0	0	0	0	NaN	NaN	NaN
2	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	3	DA Warner	S Dhawan	TS Mills	0	...	0	0	0	0	4	0	4	NaN	NaN	NaN
3	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	4	DA Warner	S Dhawan	TS Mills	0	...	0	0	0	0	0	0	0	NaN	NaN	NaN
4	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	5	DA Warner	S Dhawan	TS Mills	0	...	0	0	0	0	0	2	2	NaN	NaN	NaN

5 rows × 21 columns

#checking the rows and columns
deliveries.shape

(179078, 21)

#flitering the required details
srh = deliveries[(deliveries['match_id']==1) & (deliveries['inning']==1)]

#after filtering checking the SRH team team score
srh['batsman_runs'].value_counts()

1    57
0    32
4    17
6     9
2     9
3     1
Name: batsman_runs, dtype: int64

#checking how SRH team bats man got out
srh['dismissal_kind'].value_counts()

caught    3
bowled    1
Name: dismissal_kind, dtype: int64

#in the same way wt we used above checking the other teams score 
RCB = deliveries[(deliveries['match_id']==1) & (deliveries['inning']==2)]

RCB['batsman_runs'].value_counts()

0    49
1    44
4    15
6     8
2     7
Name: batsman_runs, dtype: int64

# checking RCB team dismissals
RCB['dismissal_kind'].value_counts()

caught     6
bowled     2
run out    2
Name: dismissal_kind, dtype: int64